Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU

Kong, Guilan; Lin, Ke; Hu, Yonghua

doi:10.1186/s12911-020-01271-2

Cited by 108 publications

(68 citation statements)

References 51 publications

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“…As mentioned above, sepsis prediction on the ICU is an important and timely problem and an active area of research. Under these circumstances, it is not surprising that some approaches have been developed in parallel to this work [33][34][35][36][37][38][39][40][41][42][43]. It will be an exciting and important avenue for future work to benchmark all these approaches (including ours) against each other and to compare their performances on a unified and realistic set of sepsis labels, for instance, the ones we propose in this work.…”

Section: Parallel Work On Sepsis Predictionmentioning

confidence: 97%

MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis

Rosnati

Fortuin

2021

PLoS ONE

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With a mortality rate of 5.4 million lives worldwide every year and a healthcare cost of more than 16 billion dollars in the USA alone, sepsis is one of the leading causes of hospital mortality and an increasing concern in the ageing western world. Recently, medical and technological advances have helped re-define the illness criteria of this disease, which is otherwise poorly understood by the medical society. Together with the rise of widely accessible Electronic Health Records, the advances in data mining and complex nonlinear algorithms are a promising avenue for the early detection of sepsis. This work contributes to the research effort in the field of automated sepsis detection with an open-access labelling of the medical MIMIC-III data set. Moreover, we propose MGP-AttTCN: a joint multitask Gaussian Process and attention-based deep learning model to early predict the occurrence of sepsis in an interpretable manner. We show that our model outperforms the current state-of-the-art and present evidence that different labelling heuristics lead to discrepancies in task difficulty. For instance, when predicting sepsis five hours prior to onset on our new realistic labels, our proposed model achieves an area under the ROC curve of 0.660 and an area under the PR curve of 0.483, whereas the (less interpretable) previous state-of-the-art model (MGP-TCN) achieves 0.635 AUROC and 0.460 AUPR and the popular commercial InSight model achieves 0.490 AUROC and 0.359 AUPR.

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Section: Parallel Work On Sepsis Predictionmentioning

confidence: 97%

MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis

Rosnati

Fortuin

2021

PLoS ONE

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“…First, our results showed that MIMIC-III specific machine learning models using only 10 clinical variables outperformed nine commonly used severity scoring methods. Several related studies (e.g., [35-37]) have also shown that machine learning models outperform severity scores on predicting in-hospital mortality. Second, developing health system specific (or local) prediction models enables continuous improvements of the model by including more training data (as more data become available), adding new clinical or laboratory variables to the model, or re-training the model using newly developed machine learning algorithms.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

OASIS+: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality

EL-Manzalawy¹,

Abbas²,

Hoaglund

et al. 2021

Preprint

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Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called “weights” or “subscores”) into a final score (or probability) for quantifying the severity of critical illness (or the likelihood of in-hospital mortality). Although these simple additive models are human readable and interpretable, their predictive performance needs to be further improved. To address this need, we argue for replacing these simple additive models with models based on state-of-the-art non-linear supervised learning algorithms (e.g., Random Forest (RF) and eXtreme Gradient Boosting (XGB)). Specifically, we present OASIS+, a variant of the Oxford Acute Severity of Illness Score (OASIS) in which an ensemble of 200 decision trees is used to predict in-hospital mortality based on the 10 same clinical variables in OASIS. Using a test set of 9566 admissions extracted from MIMIC-III database, we show that the performance of OASIS can be substantially improved from AUC score of 0.77 to 0.83 using OASIS+. Moreover, we show that OASIS+ has superior performance compared to eight other commonly used severity scoring methods. Our results underscore the potential of improving existing severity scores by using more sophisticated machine learning algorithms (e.g., ensemble of non-linear decision tress) not just via including additional physiologic measurements.

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“…The threat of hospital mortality was higher in the latter group (18.7%) compared to the former group (8.4%). The use of the least absolute shrinkage and selection operator (LASSO), random forest (RF), gradient boosting machine (GBM), and the traditional logistic regression (LR) algorithms has been proposed by Kong et al (2020) [45], to predict the risk of in-hospital death for sepsis patients. Data were collected from MIMIC III as an ICU database including the diagnostic codes, vital signs, laboratory tests, demographics, and some other clinical characteristics of each patient between 18 and 90 years old.…”

Section: Literature Reviewmentioning

confidence: 99%

The prediction of mortality influential variables in an intensive care unit: a case study

Khajehali

Tarokh

2021

Pers Ubiquit Comput

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The intensive care units (ICUs) are among the most expensive and essential parts of all hospitals for extremely ill patients. This study aims to predict mortality and explore the crucial factors affecting it. Generally, in the health care systems, having a fast and precise ICU mortality prediction for patients plays a key role in care quality, resulting in reduced costs and improved survival chances of the patients. In this study, we used a medical dataset, including patients' demographic details, underlying diseases, laboratory disorder, and LOS. Since accurate estimates are required to have optimal results, various data pre-processings as the initial steps are used here. Besides, machine learning models are employed to predict the risk of mortality ICU discharge. For AdaBoost model, these measures are considered AUC= 0.966, sensitivity (recall) = 87.88%, Kappa=0.859, F-measure = 89.23% making it, AdaBoost, accounts for the highest rate. Our model outperforms other comparison models by using various scenarios of data processing. The obtained results demonstrate that the high mortality can be caused by underlying diseases such as diabetes mellitus and high blood pressure, moderate Pulmonary Embolism Wells Score risk, platelet blood count less than 100000 (mcl), hypertension (HTN), high level of Bilirubin, smoking, and GCS level between 6 and 9.

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Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU

Cited by 108 publications

References 51 publications

MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis

MGP-AttTCN: An interpretable machine learning model for the prediction of sepsis

OASIS+: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality

The prediction of mortality influential variables in an intensive care unit: a case study

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